论文标题
基于位置感知的模仿环境的深度加固学习MMWave Mimo Systems
Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems
论文作者
论文摘要
可重新配置的智能表面（RIS）最近获得了人们的知名度，作为一种有前途的解决方案，可通过更少的硬件成本和能耗来提高无线通信的信号传输质量。这封信提供了一种新颖的深钢筋学习（DRL）算法，该算法基于RIS AID的MMWave多输入多输出系统的联合波束形成设计的位置感知环境。具体而言，我们设计了一个神经网络，以基于用户位置与MMWave通道之间的几何关系模仿传输环境。此后，开发了一种基于DRL的新型方法，该方法使用易于使用的位置信息与模仿环境进行交互。最后，模拟结果表明，与现有基于DRL的方法相比，所提出的基于DRL的算法在没有过多的交互作用的情况下提供了更强的性能。
Reconfigurable intelligent surface (RIS) has recently gained popularity as a promising solution for improving the signal transmission quality of wireless communications with less hardware cost and energy consumption. This letter offers a novel deep reinforcement learning (DRL) algorithm based on a location-aware imitation environment for the joint beamforming design in an RIS-aided mmWave multiple-input multiple-output system. Specifically, we design a neural network to imitate the transmission environment based on the geometric relationship between the user's location and the mmWave channel. Following this, a novel DRL-based method is developed that interacts with the imitation environment using the easily available location information. Finally, simulation results demonstrate that the proposed DRL-based algorithm provides more robust performance without excessive interaction overhead compared to the existing DRL-based approaches.
